PIC v1.3: Comprehensive R package for computing permafrost indices with daily weather observations and atmospheric forcing over the Qinghai-Tibet Plateau

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Abstract

An R package permafrost indices computing (PIC) was developed, which integrates meteorological observations, remote sensing data, and field measurements to compute the factors or indices of permafrost and seasonal frozen soil. At present, 16 temperature/depth-related indices are integrated into the R package PIC to estimate the possible change trends of frozen soil in the Qinghai–Tibet Plateau (QTP). These indices include the mean annual air temperature, mean annual ground surface temperature, mean annual ground temperature, seasonal thawing/freezing n factor (nt/nf), thawing/freezing degree-days of air and ground surface (DDTa/DDTs/DDFa/DDFs), temperature at the top of the permafrost, active layer thickness, and maximum seasonal freeze depth. The PIC package supports two computational modes, namely, the stations and region calculation that enables statistical analysis and intuitive visualization on the time series and spatial simulations. Over 10 statistical methods were adopted to evaluate these indices in stations, and a sequential Mann-Kendall trend test and spatial trend method were adopted. Multiple visual manners display the temporal and spatial variabilities on the stations and region. The data sets of 52 weather stations and a central region of QTP were prepared and simulated to evaluate the temporal–spatial change trends of permafrost with the climate. Simulation results show extensive permafrost degradation in QTP, and the temporal–spatial trends of the permafrost conditions in QTP were consistent with those of previous studies. The PIC package will serve engineering applications and can be used to assess the impact of climate change on permafrost.

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Luo, L., Zhang, Z., Ma, W., Yi, S., & Zhuang, Y. (2018). PIC v1.3: Comprehensive R package for computing permafrost indices with daily weather observations and atmospheric forcing over the Qinghai-Tibet Plateau. Geoscientific Model Development, 11(6), 2475–2491. https://doi.org/10.5194/gmd-11-2475-2018

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